{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "%matplotlib inline\n",
    "\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf\n",
    "import pickle as pk\n",
    "import numpy as np\n",
    "import sklearn.manifold as man\n",
    "from tensorflow.python.framework import ops\n",
    "\n",
    "from model import Emoji2Vec, ModelParams\n",
    "from phrase2vec import Phrase2Vec\n",
    "from utils import build_kb, get_examples_from_kb, generate_embeddings, get_metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Initializations\n",
    "This step takes a while to execute, wait for 'DONE'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Constants and Hyperparameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./results/unicode/k-300_pos-4_rat-1_ep-40_dr-0/emoji2vec.bin\n"
     ]
    }
   ],
   "source": [
    "word2vec_path = './data/word2vec/GoogleNews-vectors-negative300.bin'\n",
    "mapping_path = 'emoji_mapping.p'\n",
    "data_folder = './data/training/'\n",
    "embeddings_file = 'generated_embeddings.p'\n",
    "\n",
    "in_dim = 300   # Length of word2vec vectors\n",
    "out_dim = 300  # Desired dimension of output vectors\n",
    "pos_ex = 4\n",
    "neg_ratio = 1\n",
    "max_epochs = 40\n",
    "dropout = 0.0\n",
    "\n",
    "params = ModelParams(in_dim=in_dim, out_dim=out_dim, pos_ex=pos_ex, max_epochs=max_epochs,\n",
    "                    neg_ratio=neg_ratio, learning_rate=0.001, dropout=dropout, class_threshold=0.5)\n",
    "\n",
    "\n",
    "ckpt_path = params.model_folder('unicode') + '/model.ckpt'\n",
    "e2v_path = params.model_folder('unicode') + '/emoji2vec.bin'\n",
    "print(e2v_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Build Knowledge Base"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "reading training data from: ./data/training/\n"
     ]
    }
   ],
   "source": [
    "print('reading training data from: ' + data_folder)\n",
    "train_kb, ind2phr, ind2emoj = build_kb(data_folder)\n",
    "\n",
    "pk.dump(ind2emoj, open(mapping_path, 'wb'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read or Generate Embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading embeddings...\n",
      "DONE\n"
     ]
    }
   ],
   "source": [
    "embeddings_array = generate_embeddings(ind2phr=ind2phr, kb=train_kb, embeddings_file=embeddings_file,\n",
    "                                             word2vec_file=word2vec_path)\n",
    "print('DONE')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initialize models and mappings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initializing: reading embedding data from: ./data/word2vec/GoogleNews-vectors-negative300.bin\n",
      "DONE\n"
     ]
    }
   ],
   "source": [
    "print('Initializing: reading embedding data from: ' + word2vec_path)\n",
    "# get the vector for a phrase\n",
    "phraseVecModel = Phrase2Vec.from_word2vec_paths(params.in_dim, word2vec_path, e2v_path)\n",
    "print('DONE')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DONE\n"
     ]
    }
   ],
   "source": [
    "ops.reset_default_graph()\n",
    "\n",
    "# mapping from id to emoji\n",
    "mapping = pk.load(open(mapping_path, 'rb'))\n",
    "# mapping from emoji to id\n",
    "inv_map = {v: k for k, v in mapping.items()}\n",
    "\n",
    "# tensorflow model\n",
    "model = Emoji2Vec(params, len(mapping), embeddings_array=embeddings_array)\n",
    "print('DONE')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# initialize tensorflow session\n",
    "session = tf.Session()\n",
    "saver = tf.train.Saver()\n",
    "saver.restore(session, ckpt_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Performance Measures\n",
    "Check the accuracy, f1 score, auc, and the auc graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def measures(example_type):\n",
    "    train_kb, ind2phr, ind2emoj = build_kb(data_folder)\n",
    "    ex_set = get_examples_from_kb(kb=train_kb, example_type=example_type)\n",
    "\n",
    "    # evaluate the dev. accuracy using this as the threshold\n",
    "    thresh = 0.5\n",
    "\n",
    "    acc = model.accuracy(session=session, dset=ex_set, threshold=thresh)\n",
    "    f1 = model.f1_score(session=session, dset=ex_set)\n",
    "    print(str.format('Accuracy at thresh={}: {}', thresh, f1))\n",
    "    print(str.format('F1 score: {}', f1))\n",
    "    \n",
    "    try:\n",
    "        auc = model.auc(session=session, dset=ex_set)\n",
    "\n",
    "\n",
    "        print(str.format('AUC score: {}', auc))\n",
    "\n",
    "        fpr, tpr, thresholds = model.roc_vals(session=session, dset=ex_set)\n",
    "\n",
    "        fig = plt.figure()\n",
    "        ax = fig.add_subplot(111)\n",
    "        ax.plot(fpr, tpr)\n",
    "        ax.set_title(\"ROC Curve for learned emoji\")\n",
    "        plt.xlabel(\"false positive rate\")\n",
    "        plt.ylabel(\"true positive rate\")\n",
    "\n",
    "        #\n",
    "        #for i , val in enumerate(thresholds):\n",
    "        #    if i % 10 == 0:\n",
    "        #        plt.annotate(val, (fpr[i], tpr[i]))\n",
    "\n",
    "        plt.grid()\n",
    "        plt.show()\n",
    "    except:\n",
    "        print('Can\\'t compute AUC or ROC')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Set\n",
      "Accuracy at thresh=0.5: 0.9933345343181408\n",
      "F1 score: 0.9933345343181408\n",
      "Can't compute AUC or ROC\n"
     ]
    }
   ],
   "source": [
    "print('Train Set')\n",
    "measures('train')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dev Set\n",
      "Accuracy at thresh=0.5: 0.8\n",
      "F1 score: 0.8\n",
      "AUC score: 0.9160159999999999\n"
     ]
    },
    {
     "data": {
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J9AvZvK58Gbi10IjMzKwqyzU6gE6SdgeOBnbtapmxY8cyfPhwAIYMGcKIESMY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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x235986a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('Dev Set')\n",
    "measures('dev')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Set\n",
      "Accuracy at thresh=0.5: 0.8362831858407079\n",
      "F1 score: 0.8362831858407079\n",
      "AUC score: 0.9308\n"
     ]
    },
    {
     "data": {
      "image/png": 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Z9dGKFIhCh5giYomko4FrSf2OX0fEHElHppfjfOBkYDjwM0kCFkfEuCLjMjOz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\n/wcVAndbNpCtnQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1110877b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('Test Set')\n",
    "measures('test')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# RESET THE GRAPH\n",
    "ops.reset_default_graph()\n",
    "model = Emoji2Vec(params, len(mapping), embeddings_array=None, use_embeddings=False)\n",
    "\n",
    "session = tf.Session()\n",
    "saver = tf.train.Saver()\n",
    "saver.restore(session, ckpt_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Top Emoji Query\n",
    "Set `phr` as a phrase, and get the top `N` emojis correlating to that phrase."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "😃 [ 0.964535]\n",
      "😀 [ 0.96163309]\n",
      "😊 [ 0.95175558]\n",
      "☺️ [ 0.94744557]\n",
      "😰 [ 0.86711448]\n"
     ]
    }
   ],
   "source": [
    "phr = 'happy face'\n",
    "N = 5\n",
    "\n",
    "# get the vector representaiton\n",
    "vec = phraseVecModel[phr]\n",
    "\n",
    "# query the tensorflow model\n",
    "res = list()\n",
    "for colIx in range(0, len(mapping)):\n",
    "    predict = session.run(model.prob, feed_dict={\n",
    "        model.col: np.array([colIx]),\n",
    "        model.orig_vec: np.array([vec])\n",
    "    })\n",
    "    res.append(predict)\n",
    "\n",
    "# print the top N emoji\n",
    "for ind in sorted(range(len(res)), key=lambda i: res[i], reverse=True)[:N]:\n",
    "    print(mapping[ind], res[ind])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Top Phrase Query\n",
    "Set `em` as an emoji, and get the top `N` phrases correlating to that emoji."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "queen\t0.4512927532196045\n",
      "king\t0.4388968348503113\n",
      "M.Kenseth_###-###\t0.4338124394416809\n",
      "----------_-----------------------------------------------_GS##\t0.42637115716934204\n",
      "E.Sadler_###-###\t0.41635435819625854\n",
      "J.McMurray_###-###\t0.4102250337600708\n",
      "Ky.Busch_##-###\t0.4100269675254822\n",
      "HuMax_IL8_TM\t0.40330684185028076\n",
      "T.Stewart_##-###\t0.4017273485660553\n",
      "K.Kahne_###-###\t0.3957677483558655\n"
     ]
    }
   ],
   "source": [
    "# input\n",
    "em = '👑'\n",
    "N = 10\n",
    "\n",
    "# get the relevant vectors from tensorflow\n",
    "emoji_vecs = session.run(model.V)\n",
    "vec = emoji_vecs[inv_map[em]]\n",
    "\n",
    "# print top N phrases\n",
    "for word, score in phraseVecModel.from_emoji([vec], top_n=N):\n",
    "    print(str.format(\"{}\\t{}\", word, score))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analogy Task\n",
    "Set `base` as a base emoji, `minus` as an emoji to subtract from the base, `plus` as an emoji to add, and get the top `N` correlating phrases and emojis relating to this analogy. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def print_analogy_result(base, minus, plus):\n",
    "    emoji_vecs = session.run(model.V)\n",
    "    total = phraseVecModel[base] - phraseVecModel[minus] + phraseVecModel[plus]\n",
    "    \n",
    "    res = list()\n",
    "    for colIx in range(0, len(mapping)):\n",
    "        predict = session.run(model.prob, feed_dict={\n",
    "            model.col: np.array([colIx]),\n",
    "            model.orig_vec: np.array([total / np.linalg.norm(total)])\n",
    "        })\n",
    "        res.append(predict)\n",
    "        \n",
    "    ems = sorted(range(len(res)), key=lambda i: res[i], reverse=True)[:5]\n",
    "    print(str.format('{} - {} + {} = {}', base, minus, plus, [mapping[em] for em in ems]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "👑 - 🚹 + 🚺 = ['👸', '👑', '🏰', '💂', '🎎']\n",
      "💵 - 🇺🇸 + 🇬🇧 = ['💵', '💷', '💶', '💴', '💲']\n",
      "💵 - 🇺🇸 + 🇪🇺 = ['💵', '💶', '💴', '💷', '🏧']\n",
      "👦 - 👨 + 👩 = ['👪', '👩', '👸', '🍼', '👵']\n",
      "👪 - 👦 + 👧 = ['👪', '👭', '👸', '🚺', '🍼']\n",
      "🕶 - ☀️ + ⛈ = ['⛈', '🌆', '👹', '🔍', '😟']\n"
     ]
    }
   ],
   "source": [
    "print_analogy_result('👑', '🚹', '🚺')\n",
    "print_analogy_result('💵', '🇺🇸', '🇬🇧')\n",
    "print_analogy_result('💵', '🇺🇸', '🇪🇺')\n",
    "print_analogy_result('👦', '👨', '👩')\n",
    "print_analogy_result('👪', '👦', '👧')\n",
    "print_analogy_result('🕶', '☀️', '⛈')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 10 matching phrases:\n",
      "\n",
      "queen\t0.4222303628921509\n",
      "----------_-----------------------------------------------_GS##\t0.37366756796836853\n",
      "E.Sadler_###-###\t0.3566628098487854\n",
      "HuMax_IL8_TM\t0.3483329117298126\n",
      "By_BARRY_RUBIN\t0.34455251693725586\n",
      "M.Kenseth_###-###\t0.33918794989585876\n",
      "K.Kahne_###-###\t0.3325173258781433\n",
      "Ky.Busch_##-###\t0.33232730627059937\n",
      "J.McMurray_###-###\t0.33023694157600403\n",
      "T.Stewart_##-###\t0.3282149136066437\n",
      "\n",
      "Top 10 matching emoji:\n",
      "\n",
      "👸 [ 0.99603832]\n",
      "👑 [ 0.9942379]\n",
      "🏰 [ 0.9935348]\n",
      "💂 [ 0.97601867]\n",
      "🚺 [ 0.97107917]\n",
      "🎎 [ 0.96693647]\n",
      "🏇 [ 0.96228433]\n",
      "👪 [ 0.95918316]\n",
      "🐞 [ 0.95408094]\n",
      "🍮 [ 0.94833702]\n"
     ]
    }
   ],
   "source": [
    "# input\n",
    "base = '👑'\n",
    "# base = '👨'\n",
    "minus = '🚹'\n",
    "plus = '🚺'\n",
    "N = 10\n",
    "\n",
    "# get the relevant vectors from tensorflow\n",
    "emoji_vecs = session.run(model.V)\n",
    "total = emoji_vecs[inv_map[base]] - emoji_vecs[inv_map[minus]] + emoji_vecs[inv_map[plus]]\n",
    "\n",
    "# print the top N phrases\n",
    "print(str.format('Top {} matching phrases:', N))\n",
    "print()\n",
    "for word, score in phraseVecModel.from_emoji([total], top_n=N):\n",
    "    print(str.format(\"{}\\t{}\", word, score))\n",
    "    \n",
    "# query the tensorflow model\n",
    "res = list()\n",
    "for colIx in range(0, len(mapping)):\n",
    "    predict = session.run(model.prob, feed_dict={\n",
    "        model.col: np.array([colIx]),\n",
    "        model.orig_vec: np.array([total / np.linalg.norm(total)])\n",
    "    })\n",
    "    res.append(predict)\n",
    "\n",
    "# print the top N emoji\n",
    "print()\n",
    "print(str.format('Top {} matching emoji:', N))\n",
    "print()\n",
    "for ind in sorted(range(len(res)), key=lambda i: res[i], reverse=True)[:N]:\n",
    "    print(mapping[ind], res[ind])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Visualize Emoji Vector Space\n",
    "2-D projection of the Emoji vector space, using t-SNE.\n",
    "\n",
    "Jupyter won't plot emoji. Use visualize.py to see a clearer picture."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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9+8jPz6eqqopNmzbhcrnqBAu9/PLLANjtdiwWC5qmUV5ezpEjR/y66pOhJnir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S8vj0OHDnHmzBk0TaNWrVokJycTFBTEjz/+SHZ2NlFRkfTr15cBAwYQFxd3wxqPvLw8rl69isdz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\nLCx+JPwfEJoxgCL2djMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fe5acc50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "V = session.run(model.V)\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "tsne = man.TSNE(perplexity=30, n_components=2, init='pca', n_iter=5000)\n",
    "trans = tsne.fit_transform(V)\n",
    "x, y = zip(*trans)\n",
    "plt.scatter(x, y)\n",
    "\n",
    "for i in range(len(trans)):\n",
    "    ax.annotate(mapping[i], xy=trans[i], textcoords='data')\n",
    "\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
